Geolocation is the determination of the geographic location of an object through the use of electromagnetic signals, digital mapping, image processing techniques or other data fusion methods aided by electronic communication media. Originally developed for military use, wireless geolocation techniques now finds use in day-to-day commercial and personal activities, like geolocation of illegal transmitters, tracking down cellular phones and radio devices, etc. Conventional radio aided geolocation methods employ parameters of a received signal like the Angle of Arrival (AOA), Time of Arrival (TOA), Time Difference of Arrival (TDOA), Received Signal Strength (RSS) and Frequency Difference of Arrival (FDOA) etc. to determine the position of the radio emitter through triangulation.
Hybrid geolocation techniques employ a combination of two or more parameters like AOA-TDOA, RSS-TOA, TDOA-FDOA, RSS-TDOA etc. and offer a higher resolution in the estimation of the location of the radio emitter since they employ more than one parameter within the received signal. Such hybrid techniques can be implemented using mobile-based, sensor network based or radar based platforms.
However, even hybrid techniques, for example the AOA-TDOA estimation, pose unique challenges when used in multi-path environments like urban areas. In a typical multi-path environment, one or more sensors receive signals from a plurality of emitters from multiple directions and they estimate the AOA and the TOA information based on these received signals. This is achieved through processing the captured signal on the antenna array and using resolution techniques to estimate the data parameters. But, the urban area multi-path environment is highly time varying and heavily cluttered giving rise to significant multi-path fading. Invariably, there is an absence of a line-of-sight path between one or more emitters and a receiver, and the received signal from a particular emitter arrives at a sensor through multiple directions. In the absence of line-of-sight, the triangulation approach to position estimation does not work effectively. Consequently, most of the conventional and hybrid geolocation techniques fail to deliver an accurate estimate on the emitter location. Thus, a primary hurdle to localization or geolocation of radio emitters is the non-line-of-sight (NLOS) conditions between sensor and emitter.
In addition to the above mentioned problem on NLOS conditions and multi-path fading, the presence of multiple emitters operating on the same frequency spectrum leads to further complications. Due to the presence of multiple emitters it is vital for the sensor to identify the signal contribution of a single emitter within the mixture of signals received from several emitters. Only after such a discrimination of the emitters, can the geolocation techniques be used to estimate the locations of all the individual emitters. Accordingly, there also exists a unique challenge in identifying the contribution from each emitter in a multi-path environment.
Most prior art systems and techniques, in order to discriminate between distinct emitters, make a priori assumptions on the number of emitters present in the multi-path environment and the characteristics of the received signal. Subsequent to making a priori assumptions on the number of emitters, clustering techniques are employed on the multi-path data received at the sensors to determine contribution from the assumed emitters. Since there is a priori assumption on the number of emitters in a environment, the process of discriminating contribution from actual number of emitters is often not accurate. Therefore, the current geolocation methods and techniques are not effective in discriminating the correct number of emitters and consequently result in inaccurate or poor location tracking capability.
In view of the above, there exists a need for developing a robust clustering technique which will classify and attribute the received signal contributions to each individual emitter. Specifically, there is a need to develop an unsupervised mixture component analysis technique, which can be used to effectively estimate the parameters like AOA and TOA directly from the received mixture of multi-path data without relying upon any assumptions. This will facilitate a directly data-driven emitter discrimination approach in a multi-path environment.
Further, there exists a need to optimize the power consumption of a sensor network. The present invention aims at providing a cognitive sensor activation framework to selectively activate one or more sensors which are in proximity to the emitters, which results in power savings and noise reduction within the sensor network.